On Convergence of Minimization Methods: Attraction, Repulsion and Selection

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dc.contributor.authorZhang, Yinen_US
dc.contributor.authorTapia, Richarden_US
dc.contributor.authorVelazquez, Leticiaen_US
dc.date.accessioned2018-06-18T17:47:33Zen_US
dc.date.available2018-06-18T17:47:33Zen_US
dc.date.issued1999-03en_US
dc.date.noteMarch 1999 (Revised August 1999)en_US
dc.description.abstractIn this paper, we introduce a rather straightforward but fundamental observation concerning the convergence of the general iteration process. x^(k+1) = x^k - alpha(x^k) [B(x^k)]^(-1) gradĀ­f(x^k) for minimizing a function f(x). We give necessary and sufficient conditions for a stationary point of f(x) to be a point of strong attraction of the iteration process. We will discuss various ramifications of this fundamental result, particularly for nonlinear least squares problems.en_US
dc.format.extent18 ppen_US
dc.identifier.citationZhang, Yin, Tapia, Richard and Velazquez, Leticia. "On Convergence of Minimization Methods: Attraction, Repulsion and Selection." (1999) <a href="https://hdl.handle.net/1911/101917">https://hdl.handle.net/1911/101917</a>.en_US
dc.identifier.digitalTR99-12en_US
dc.identifier.urihttps://hdl.handle.net/1911/101917en_US
dc.language.isoengen_US
dc.titleOn Convergence of Minimization Methods: Attraction, Repulsion and Selectionen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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